When people were talking more on GPT4, BARD and other LLMs slowly Github Copilot is eating the software programming market. Github comes with 2 critical releases one Codespace and other Copilot. Codespace is a development environment in cloud. You can customize your project for GitHub Codespaces by committing configuration files to your repository (often known as Configuration-as-Code), which creates a repeatable codespace configuration for all users of your project.
This post I will talk more about Github Copilot. GitHub Copilot is a cloud-based artificial intelligence tool developed by GitHub and OpenAI (who built the current ChatGPT/GPT4) to assist users of Visual Studio Code, Neovim, and JetBrains integrated development environments (IDEs) by autocompleting those code. Currently it works best for users coding in Python, JavaScript, TypeScript, Ruby, and Go.
The concept behind Github Copilot is simple: it uses machine learning algorithms to analyze your code and provide suggestions for completing it. These suggestions can range from simple code snippets to entire functions or classes. The goal is to help developers write code faster and with fewer errors, by automating many of the tedious and repetitive tasks that come with coding.
But does Github Copilot live up to the hype? In this blog post, we’ll explore the features and limitations of Github Copilot, and provide examples of how it can be used to improve your coding workflow.
How Does Github Copilot Work?
Github Copilot is integrated into Microsoft’s Visual Studio Code and GitHub’s CodeSpaces, and it can be installed as a VS Code extension. Once installed, you can activate it by selecting the “Copilot: Insert suggestion” command from the VS Code command palette, or by using a keyboard shortcut.
When you activate Copilot, it analyzes the context of your code and generates suggestions for completing it. These suggestions are based on the code you’ve already written, as well as on a massive dataset of code snippets and programming patterns. Copilot uses deep learning algorithms to generate these suggestions, and it improves over time as more developers use it and provide feedback.
Github Copilot supports a wide range of programming languages, including Python, JavaScript, TypeScript, Ruby, Go, and Rust. It can generate suggestions for a variety of tasks, such as creating classes, functions, and variables, as well as providing code snippets for common programming patterns.
However, it’s important to note that GitHub Copilot is not a replacement for human developers. It still requires the developer to understand the code they are writing and make decisions about how to structure it and handle errors. Additionally, because it has been trained on existing code, it may not always produce the most efficient or secure code, so it’s important for developers to review and test the code generated by GitHub Copilot before using it in production.
Example 1: Generating a Flask App
Suppose you’re developing a web application using Flask, a popular Python web framework.
You need to create a new Flask app, but you’re not sure how to get started. With Copilot, you can generate a new Flask app with just a few clicks. Here are the steps to do that.
1. First, create a new Python file and type “from flask import Flask“.
2. Then, you activate Copilot and select the “Flask app” suggestion.
3. Github Copilot generates the following code:
This code creates a new Flask app with a route that returns “Hello, World!” when you visit the root URL. With this code as a starting point, you can customize your Flask app to suit your needs.
Example 2: Completing a Function
Suppose you’re writing a Python function that calculates the sum of a list of numbers. You’ve written the following code
You’re not sure how to complete the function to return the total sum of the list. With Github Copilot, you can generate the missing code with just a few clicks. Here is how.
1. You activate Github Copilt.
2. Select the “Sum list” suggestion.
3. Github Copilot generates the following code:
This code completes the function by adding a “return” statement that returns the total sum.
Conclusion
In summary, GitHub Copilot is a powerful tool that can help developers write code faster and more efficiently. Its ability to suggest code completions and generate code snippets based on natural language commands makes it a valuable addition to any developer’s toolkit. If you haven’t tried it yet, we encourage you to give it a try and see how it can improve your coding workflow.
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